FindA University Ltd Featured PhD Programmes
University of Leeds Featured PhD Programmes
FindA University Ltd Featured PhD Programmes

Interpretable machine learning for the medical domain

Faculty of Natural Sciences

About the Project

Machine learning methods have been successfully applied to a wide range of diverse domains, from image classification to natural language processing, and they have often achieved impressive performances especially since the advent of deep learning. However, when high stakes decisions are to be taken based on the outcome of an algorithm, relying on common accuracy metrics is no longer sufficient.
This is the case in the medical domain: predictions potentially affecting human lives require a high level of accountability, and transparency. Unfortunately, many machine learning algorithms are still poorly understood by non-specialists, mostly because of their inherent black-box nature.

The PhD research will follow an innovative approach known as interpretable machine learning and the aim of the research will be to build explainable models for the prediction for the quality of life of patients diagnosed with cancer. In collaboration with colleagues from medical statistics, the candidate will study the impact of model features on the model outcome to provide retrospective explanation of the prediction.

The research will be supervised by Dr Marco Ortolani in the Centre for Computer Science Research at Keele University, potentially with other national and international project partners.

Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants should have good computing skills, an interest in machine learning, and passion to apply computer science to contexts where social benefit is important. They should be self-motivated and have the ability to work both independently and as part of a team.

This opportunity is open to UK/EU and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.
For information regarding University tuition fees please see:

To apply please go to

Please quote FNS GS 2020-06 on your application.

Keele University values diversity, and is committed to ensuring equality of opportunity. In support of these commitments, Keele University particularly welcomes applications from women and from individuals of black and ethnic minority backgrounds for this post. The School of
Computing and Mathematics and Keele University have both been awarded Athena Swan awards and Keele University is a member of the Disability Confident scheme. More information is available on these web pages:

Funding Notes

Open to fully self-funded students only.

Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3 year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants (1st class honours), however, none are currently available.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to Keele University will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully

Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.

FindAPhD. Copyright 2005-2020
All rights reserved.